Data Management and Analysis Core (DMAC)

数据管理和分析核心 (DMAC)

• 批准号: 10352515
• 负责人: Harrison Dekker
• 金额: $ 23.06万
• 依托单位: UNIVERSITY OF RHODE ISLAND
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10352515
• 关键词: Address; Adopted; Bayesian Modeling; Biometry; Code; Collaborations; Communities; Computing Methodologies; Data; Data Analyses; Data Analytics; Data Science; Development; Discipline; Disease; Documentation; Ensure; Environmental Health; FAIR principles; Feedback; Goals; Growth; Health; High Performance Computing; Information Storage; Infrastructure; Link; Machine Learning; Mass Spectrum Analysis; Mathematics; Metadata; Methodology; Methods; Mission; Modeling; Modernization; Monitor; National Institute of Environmental Health Sciences; Outcome; Policy Maker; Preparation; Procedures; Process; Protocols documentation; Quality Control; Reproducibility; Research; Research Design; Research Personnel; Research Project Grants; Resource Sharing; Resources; Running; Science; Series; Signal Recognition Particle; Source; Statistical Data Interpretation; Statistical Methods; Structure; Time; Training; Training Support; Universities; Work; community engagement; complex data; computing resources; cost; data exploration; data integration; data interoperability; data management; data quality; data repository; data reuse; data science infrastructure; data sharing; data sharing networks; data visualization; design; experience; flexibility; improved; innovation; member; method development; open data; power analysis; quality assurance; repository; skills; sound; statistics; tool; user-friendly

PROJECT SUMMARY/ABSTRACT – DATA MANAGEMENT AND ANALYSIS CORE (DMAC) To understand the link between PFAS exposure and disease, there is a need for data integration from a broad range of scientific disciplines and for researchers to acknowledge the importance of the entire lifecycle of the data in a context beyond their immediate research objective. The long-term goal is to establish a data science infrastructure that promotes best practice, i.e., high-quality data that are Findable, Accessible, Interoperable, and Reusable (FAIR), and that will be easily applicable to other interdisciplinary team projects. DMAC’s overall objective is to work closely with all STEEP project members and equip them with low-cost, user-friendly, FAIR- integrated processes, as well as cutting-edge statistical and computing methods. Guided by the team’s experience, DMAC will pursue four specific aims: (i) develop, coordinate, and monitor a user-friendly, easily- accessible infrastructure and processes for creating, storing, and sharing data and metadata, irrespective of size, both internally and publicly, (ii) address metadata needs across all STEEP research data products, (iii) provide integrative methodological and computational support, as well as develop mission-oriented methods, and (iv) develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP projects. The approach is innovative because it departs from the status quo by providing: (i) an easy-to- implement, modern, and integrative data management infrastructure that is compliant with all FAIR principles and QA/QC, (ii) cutting-edge statistical methods (e.g., causal inference, Bayesian, and time series models) to draw mathematically-precise inferences from complex data structures (e.g., non-randomized, longitudinal), and (iii) high-performance computing resources. The proposed research is significant because it is expected to advance and expand the use of FAIR-compliant research in the field of environmental health. Ultimately, such practice has the potential to inform policy makers with precise and reliable findings and help reduce the reproducibility crisis. STEEP’s DMAC will pursue these goals via these Specific Aims: Specific Aim 1: Develop and support infrastructure and processes for sharing data and metadata Specific Aim 2: Address metadata needs across all STEEP research data products: Specific Aim 3: Provide integrative statistical support Specific Aim 4: Develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP research projects

项目摘要/摘要 - 数据管理和分析核心（DMAC） 要了解PFAS暴露与疾病之间的联系，需要从广泛的 科学学科的范围和研究人员要承认整个生命周期的重要性 在其直接研究目标之外的上下文中的数据。长期目标是建立数据科学 促进最佳实践的基础架构，即可发现，可访问，可互操作的高质量数据 可重复使用（公平），这将很容易适用于其他跨学科团队项目。 DMAC的整体 目的是与所有陡峭的项目成员紧密合作，并为他们配备低成本，用户友好，公平 - 集成过程以及尖端的统计和计算方法。在团队的指导下 经验，DMAC将追求四个具体目标：（i）开发，协调和监视用户友好，轻松 - 可访问的基础架构以及用于创建，存储和共享数据和元数据的流程 内部和公开的大小，（ii）满足所有陡峭的研究数据产品的元数据需求，（iii） 提供综合的方法论和计算支持，以及以发展为任务的方法， （iv）在陡峭的 项目。该方法具有创新性，因为它通过提供：（i）易于 - 实施，现代和集成的数据管理基础架构，符合所有公平原则 和QA/QC，（ii）尖端统计方法（例如因果推理，贝叶斯和时间序列模型） 从复杂的数据结构（例如非随机，纵向）和 （iii）高性能计算资源。拟议的研究很重要，因为它有望 推进并扩大了在环境健康领域中符合公平研究的使用。最终，这样的 实践有可能通过精确和可靠的发现为决策者提供信息，并帮助减少 可重复性危机。陡峭的DMAC将通过这些特定目标来追求这些目标： 特定目标1：开发和支持共享数据和元数据的基础架构和流程 特定目的2：满足所有陡峭研究数据产品中元数据需求： 特定目标3：提供综合统计支持 特定目标4：制定标准并提供数据质量保证和质量控制 （QA/QC）在陡峭的研究项目中